Explore the latest peer-reviewed research with AI-generated summaries, key findings, and insights for easy understanding. All content is legally sourced from academic metadata with links to original papers.
This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. The hierarchical design and the shifted window approach al...
In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets). We implement our findings into...
Although convolutional neural networks (CNNs) have achieved great success in computer vision, this work investigates a simpler, convolution-free backbone network use-fid for many dense prediction task...
Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). We conduct experiments o...
Abstract The Central Brain Tumor Registry of the United States (CBTRUS), in collaboration with the Centers for Disease Control and Prevention (CDC) and National Cancer Institute (NCI), is the largest...
Industry 4.0, an initiative from Germany, has become a globally adopted term in the past decade. We have elected to use five of these questions to structure our arguments and tried to be unbiased for...
Transformers, which are popular for language modeling, have been explored for solving vision tasks recently, e.g., the Vision Transformer (ViT) for image classification. For example, T2T-ViT with comp...
Self-attention networks have revolutionized natural language processing and are making impressive strides in image analysis tasks such as image classification and object detection. Our Point Transform...
Object detection on drone-captured scenarios is a recent popular task. On VisDrone Challenge 2021, TPH-YOLOv5 wins 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/...
The recently developed vision transformer (ViT) has achieved promising results on image classification compared to convolutional neural networks. For example, on the ImageNet1K dataset, with some arch...
This research explores 2021 report of the Lancet Countdown on health and climate ch..., contributing new insights to the field of Medicine & Biology.
Though many attempts have been made in blind super-resolution to restore low-resolution images with unknown and complex degradations, they are still far from addressing general real-world degraded ima...
This paper does not describe a novel method. We discuss the currently positive evidence as well as challenges and open questions.
This research explores Effectiveness of mRNA BNT162b2 COVID-19 vaccine up to 6 mont..., contributing new insights to the field of Medicine & Biology.
This research explores systematic review and meta-analysis of longitudinal cohort s..., contributing new insights to the field of Medicine & Biology.
We introduce four new real-world distribution shift datasets consisting of changes in image style, image blurriness, geographic location, camera operation, and more. Overall we find that some methods...
This research explores High-entropy ceramics: Review of principles, production and..., contributing new insights to the field of Physics & Space Science.
Graph convolutional networks (GCNs) have been widely used and achieved remarkable results in skeleton-based action recognition. Combining CTR-GC with temporal modeling modules, we develop a powerful g...
This research explores physics of higher-order interactions in complex systems, contributing new insights to the field of Artificial Intelligence.
Visual surface anomaly detection aims to detect local image regions that significantly deviate from normal appearance. On the challenging MVTec anomaly detection dataset, DRÆM outperforms the current...